Structural Similarity Research Articles

Effect of soy protein isolate supplementation on the estradiol biosynthesis pathway in a female rat model

Background Soy protein isolate (SPI) has gained popularity as an alternative to animal proteins. Soy consumption may have a link to rising serum estrogen in humans. This concern is attributed to phytoestrogens, specifically isoflavones present in soy protein, acting as estrogen substitutes and modulators. Objective The objective of this research was to assess the influence of SPI supplementation on estradiol hormone levels and the expression of genes associated with estrogen synthesis in female rats. Materials and methods Female Wistar rats (n=18) were evenly divided into three groups: group 1 (normal control) received oral administration of a saline vehicle. Group 2 (low dose) received 450 mg/kg body weight of SPI for 30 days. Group 3 (high dose) received 900 mg/kg body weight of SPI for 30 days. All administrations were conducted intragastrically. Results and conclusion The results of our study indicate that there was a significant increase in the levels of estradiol hormone in groups receiving low and high doses of SPI when compared with the control group. There was an upregulation in the messenger ribonucleic acid expression of the Cyp19 gene in low-dose and high-dose groups. Moreover, the low-dose group showed upregulation in the expression of the HSD3B gene. These genes are involved in the biosynthesis pathway of estrogen hormone in females. Therefore, the use of SPI should be cautious because it contains phytoestrogens (isoflavones), which have structural similarities to endogenous female estrogen and may cause hormonal disturbance in females.

The role of surface and structural similarities in the retrieval of realistic perceptual events

AbstractThis study investigated whether structural similarities (i.e. abstract frames, e.g. once bitten twice shy) can prevail over surface similarities (i.e. contexts, e.g. restaurant) in driving the retrieval of realistic events involving dynamic, multimodal and perceptually crowded data. After watching an initial set of video clips, participants had to indicate whether a new video clip, that shared surface similarities with an initial event and structural similarities with another one, elicited a retrieval. The results of Experiment 1A showed that retrieval was more likely to be elicited by structural rather than by surface similarities. Experiment 1B confirmed that the surface similarities manipulated in this study were strong enough to elicit substantial retrievals when the competing structural match was neutralized. The pattern of results obtained in Experiment 1A remained unchanged when the number of unrelated video clips within the initial set was increased. The findings suggest that structurally based retrievals still prevail when familiar structures underlie realistic perceptual events. They open new perspectives regarding the settings that promote structurally based retrievals in educational contexts where unfamiliar principles are introduced.

Feedback projection synchronization of discrete chaotic systems and its application to speech encryption

Abstract Chaotic systems are widely used in secure communication due to their sensitivity to initial values, unpredictability, and complex motion trajectories. In this paper, we study the encryption method of chaotic synchronization and introduce a scaling factor based on traditional feedback control synchronization to achieve more accurate projection synchronization. The effectiveness and robustness of the method in chaotic systems are verified through theoretical proofs and numerical simulations. A chaotic masked speech encryption system utilizing bit similarity is designed; the structural similarity index (SSIM) of the decrypted signal with the original signal is as high as 0.992866, while the SSIM value of the encrypted signal with the original signal is only 0.000030, proving the efficiency and security of the encryption process. Additionally, we analyzed the data transmission process of the encryption system. The fusion of the control signal and the encryption sequence into one transmission sequence in the channel not only saves hardware and software design resources but also reduces inter-channel interference and conflict, improving the reliability and stability of the transmission. Experimental results show that the system performs well in terms of data transmission security and anti-interference capability.

Image enhancement with art design: a visual feature approach with a CNN-transformer fusion model

Graphic design, as a product of the burgeoning new media era, has seen its users’ requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image’s color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%–10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design.

A large-scale screening identifies receptor-like kinases with common features in kinase domains that are potentially related to disease resistance in planta

IntroductionThe plant genome encodes a plethora of proteins with structural similarity to animal receptor protein kinases, collectively known as receptor-like protein kinases (RLKs), which predominantly localize to the plasma membrane where they activate their kinase domains to convey extracellular signals to the interior of the cell, playing crucial roles in various signaling pathways. Despite the large number of members within the RLK family, to date, only a few have been identified as pattern-recognition receptors (PRRs), leaving many potential RLKs that could play roles in plant immunity undiscovered.MethodsIn this study, a recombinant strategy was initially employed to screen the kinase domains of 133 RLKs in the Arabidopsis genome to determine their involvement in the pathogen-triggered immunity (PTI) pathway. Subsequently, 6 potential immune-related recombinant RLKs (rRLKs) were selected for the creation of transgenic materials and underwent functional characterization analysis. Finally, a sequence analysis was conducted on the kinase domains of these 133 RLKs as well as the known immune RLK receptor kinase domains from other species.ResultsIt was found that 24 rRLKs activated the PTI response in Arabidopsis fls2 mutant protoplasts following flg22 treatment. Consistently, when 6 of these rRLKs were individually expressed in fls2 background, they exhibited diverse PTI signal transduction capabilities via different pathways while all retained membrane localization. Intriguingly, sequence analysis revealed multiple conserved amino acid sites within kinase domains of these experimentally identified immune-related RLKs in Arabidopsis. Importantly, these patterns are also preserved in RLKs involved in PTI in other species.DiscussionThis study, on one hand, identifies common features that theoretically can enhance our understanding of immune-related RLKs and facilitate the discovery of novel immune-related RLKs in the future. On the other hand, it provides experimental evidence for the use of recombinant technique to develop diverse rRLKs for molecular breeding, thereby conferring high resistance to plants without compromising their normal growth and development.

Melt Spinnability Comparison of Mechanically and Chemically Recycled Polyamide 6 for Plastic Waste Reuse

With the increasing volume of synthetic fiber waste, interest in plastic reuse technologies has grown. To address this issue, physical and chemical recycling techniques for polyamide, a major component of textile waste, have been developed. This study investigates the remelting and reforming properties of four types of pristine and recycled polyamide 6, focusing on how the microstructural arrangement of recycled polyamides affects polymer fiber formation. DSC and FT-IR were used to determine the thermal properties and chemical composition of the reformed thin films. Differences in the elongation behavior of molten fibers during the spinning process were also observed, and the morphology of the resulting fibers was examined via SEM. Birefringence analysis revealed that the uniformity of the molecular structure greatly influenced differences in the re-fiberization process, suggesting that chemically recycled polyamide is the most suitable material for re-fiberization with its high structural similarity to pristine polyamide.

MCRANet: MTSL-based connectivity region attention network for PD-L1 status segmentation in H&E stained images

The quantitative analysis of Programmed death-ligand 1 (PD-L1) via Immunohistochemical (IHC) plays a crucial role in guiding immunotherapy. However, IHC faces challenges, including high costs, time consumption and result variability. Conversely, Hematoxylin-Eosin (H&E) staining offers cost-effectiveness, speed, and stable results. Nonetheless, H&E staining, which solely visualizes cellular morphological features, lacks clinical applicability in detecting biomarker expressions like PD-L1. Substituting H&E staining for IHC in determining PD-L1 status is a clinically significant and challenging task. Motivated by above observations, we propose a Multi-Task supervised learning (MTSL)-based connectivity region attention network (MCRANet) for PD-L1 status segmentation in H&E stained images. To reduce interference from non-tumor areas, the MTSL-based region attention is proposed to enhances the network's capability to distinguish between tumor and non-tumor regions. Consequently, this augmentation further improves the network's segmentation efficacy for PD-L1 positive and negative regions. Furthermore, the PD-L1 expression regions demonstrate interconnection throughout the tissue section. Leveraging this topological prior knowledge, we integrate a connectivity modeling module (CM module) within the MTSL-based region attention module (MRA module) to enhance the precision of MTSL-based region attention localization. This integration further improves the structural similarity between the segmentation results and the ground truth. Extensive visual and quantitative results demonstrate that our supervised-learning-guided MRA module produces more interpretable attention and the introduced CM module provides accurate positional attention to the MRA module. Compared to other state-of-the-art networks, MCRANet exhibits superior segmentation performance with a dice similarity coefficient (DSC) of 79.6 % on the lung squamous cell carcinoma (LUSC) PD-L1 status dataset.

Bimodal PET/MRI generative reconstruction based on VAE architectures

Abstract •Objective:In this study, we explore positron emission tomography(PET)/magnetic resonance imaging (MRI) joint reconstruction within a deeplearning (DL) framework, introducing a novel synergistic method.
•Approach:We propose a new approach based on a variational autoencoder (VAE)constraint combined with the alternating direction method of multipliers (ADMM)optimization technique. We compare several VAE architectures, including jointVAE, mixture of experts (MoE) and product of experts (PoE), to determine theoptimal latent representation for the two modalities. We trained then evaluatedthe architectures on a brain PET/MRI dataset.
•Main results:We showed that our approach takes advantage of each modalitysharing information to each other, which results in improved peak signal-to-noiseratio (PSNR) and structural similarity (SSIM) as compared with traditionalreconstruction methods, particularly for short acquisition times. We find that theone particular architecture, MMJSD, is the most effective for our methodology.
•Significance:The proposed method outperforms classical approaches especiallyin noisy and undersampled conditions by making use of the two modalities together to compensate for the missing information

MFIB 2.0: a major update of the database of protein complexes formed by mutual folding of the constituting protein chains

Abstract While the majority of proteins with available structures are able to fold independently and mediate interactions only after acquiring their folded state, a subset of the known protein complexes contains protein chains that are intrinsically disordered in isolation. The Mutual Folding Induced by Binding (MFIB) database collects and classifies protein complexes, wherein all constituent protein chains would be unstable/disordered in isolation but fold into a well-defined 3D complex structure upon binding. This phenomenon is often termed as cooperative folding and binding or mutual synergistic folding (MSF). Here we present a major update to the database: we collected and annotated hundreds of new protein complexes fulfilling the criteria of MSF, leading to an almost six-fold increase in the size of the database. Many novel features have also been introduced, such as clustering of the complexes based on structural similarity and domain types, assigning different evidence levels to each entry and adding the evidence coverage label that allowed us to include complexes of multi(sub)domain monomers with partial MSF. The MFIB 2.0 database is available at https://mfib.pbrg.hu.

Influence of exposure time on the aero-optical effects of a high-speed aircraft optical window

Under high-speed conditions (Mach number > 3), the turbulent shear layer formed on the optical window causes phase changes in the light, resulting in the aero-optical effects, which significantly affect the imaging quality at different exposure times, τ. In this study, imaging data of an optical window were collected in a Mach 3.8 supersonic low-noise wind tunnel for a facula and a USAF 1951 resolution board, as well as distorted wavefront data of visible light. As τ increases from 0.2 ms to 5 ms, the centroid jitter of the facula shows an overall decreasing trend and the increase of τ mainly inhibits the jitter in the spanwise direction. While τ varies from 0.2 ms to 28 ms, structural analysis indicated that the structural similarity and stability of imaging improves and this improvement has an upper limit of τ0 = 22 ms, coined as the saturation exposure time. Normalized modulation transfer function curves derived from USAF 1951 images show a decrease in the imaging resolution as τ increases. The average wavefront distortion can be reduced by up to 71.7% as τ increases from 5 ms to 30 ms, while the standard deviation of wavefront distortion initially decreases and then stabilizes at 0.0025λ.

First-principles study on a new 2D carbon allotrope with an intrinsic wide bandgap: A comparison with α-graphyne

Nanocarbon materials with intrinsic electronic bandgaps are highly desirable for next-generation carbon-based nanoelectronics. Herein, a new two-dimensional (2D) carbon allotrope with structural similarities to α-graphyne has been proposed theoretically, which exhibits intrinsic semiconducting behavior with a wide direct bandgap significantly larger than that reported in other 2D carbon allotropes. Based on first-principles calculations, the structural and electronic properties of the new 2D carbon allotrope, as well as its lattice stability, have been systematically investigated by adopting a comparative study with α-graphyne. Moreover, the effects of vertical stacking and in-plane biaxial strain on the new 2D carbon allotrope have also been clarified in this work, providing robust approaches for the effective modulation of electronic properties in the new 2D carbon allotrope. Thus, the intrinsic wide bandgap, along with effective modulations, suggests great advantages and potentials of the new 2D carbon allotrope in carbon-based electronic devices and light-emitting applications.

Organized Capitalism and Organized Science

This article compares two different ways in which German industries, during the half century before 1914, managed to integrate useful results from scientific research: At the time, on the one hand there was single-firm-based industrial research, and on the other the cooperation of science, business, and government in the Emperor William Society for the Advancement of Sciences (1911) out of which, after the Second World War, the Max Planck Society emerged. On this basis, the article discusses similarities and tensions between capitalism and the sciences which, in spite of some structural similarities, follow different logics.

NIMG-65. SYNTHETIC MRI GENERATION TO FACILITATE AUTOSEGMENTATION OF PEDIATRIC BRAIN TUMORS IN LIMITED DATA SCENARIOS

Abstract BACKGROUND The availability of standard multiparametric MRI sequences, including pre-contrast T1w (T1w-pre), post-contrast T1w (T1w-post), T2w, and FLAIR images, is essential for accurate segmentation of tumor subregions and subsequent response assessment in pediatric brain tumors (PBTs). However, clinical MRI sets are often incomplete due to imaging artifacts or inconsistent acquisition protocols across different centers. This study leverages Generative Adversarial Networks (GANs) to synthesize missing FLAIR and T1w-pre images from T2w and T1w-post images, respectively, to facilitate tumor segmentation in PBTs. METHODS We developed two GAN models based on the pix2pix framework, trained, validated, and tested on FLAIR-T2w and T1w-post-T1w-pre pairs from a multi-histology and multi-institutional cohort of 476 subjects with PBTs from the Children’s Brain Tumor Network (CBTN). The perceptual quality of the synthesized T1w-pre and FLAIR images was evaluated using the Structural Similarity Index (SSI; ideal value = 1). The robustness of the synthesized sequences to replace missing MRI sequences was evaluated by comparing the segmentation performance (Dice score) of our pre-trained autosegmentation model using all real modalities versus replacing real sequences with their synthesized counterparts. RESULTS The T1w-pre and FLAIR synthesis GANs achieved mean SSI values of 0.95 and 0.93, respectively. Median Dice scores for segmentation of whole tumor (WT), enhancing tumor (ET), and tumor core (TC) were calculated for real sequences: 0.90, 0.83, 0.89, replacing real FLAIR with synthetic: 0.86, 0.81, 0.86, and replacing real T1w-pre with synthetic: 0.90, 0.75, 0.89. No statistical significance (p>0.05) was observed in segmentation performance for WT, TC, ET when replacing real T1w-pre with synthetic, and for ET when replacing real with synthetic FLAIR sequences. CONCLUSION This method achieves robust segmentation performance in limited data scenarios by synthesizing missing MRI sequences. Future work includes improving FLAIR synthesis and segmentation performance for WT and ET by implementing and comparing other GAN architectures.

MRI denoising with a non‐blind deep complex‐valued convolutional neural network

AbstractMR images with high signal‐to‐noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex‐valued convolutional neural network (CNN) for MRI denoising. A complex‐valued CNN incorporating the noise level map (non‐blind DnCNN) was trained with ground truth and simulated noise‐corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low‐field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real‐valued CNN and several other algorithms. For the simulated noise‐corrupted testing dataset, the complex‐valued models had superior normalized root‐mean‐square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non‐blind DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low‐field data, the non‐blind DnCNN significantly improved the SNR and visual quality of the image. The proposed non‐blind DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non‐blind DnCNN to medical imaging. The method holds the potential to enable improved low‐field MRI, facilitating enhanced diagnostic imaging in under‐resourced areas.

An Analysis of Case Study Utilizing Distributed Power Flow Controllers Which Improves and Mitigates Power Quality

Abstract—Providing high-quality electrical power should be taken into consideration in light of the rising demand for electricity and the rise in non-linear loads in power systems. This research examines power quality concerns related to voltage sag and swell and uses a distributed power flow controller (DPFC) to reduce voltage deviation and enhance power quality. A novel FACTS device, the DPFC shares structural similarities with the unified power flow controller (UPFC). Despite UPFC, DPFC separates the three-phase series converter into many single-phase series dispersed converters via the line and removes the common dc-link between the shunt and series converters. A DPFC located in a single-machine infinite bus power system with two parallel transmission lines is included in the case study. The MATLAB/Simulink environment was to model the system. The simulation findings that are shown confirm that DPFC may raise power quality.

Business Process Clustering in Small and Medium Manufacturing Industries Based on Enterprise Resource Planning Concepts: A Systematic Literature Review

Abstract Business process management has become increasingly important in the manufacturing sector, playing a vital role in fostering productivity and facilitating organizational adaptability to technological advancements. Although each company’s business process models vary in uniqueness and complexity, certain similarities can be identified from these differences. This study employs a systematic literature review to aggregate and summarize findings on business process models, modeling languages, clustering techniques, and foundational clustering principles. Results demonstrate the dearth of research on the grouping of business process models in the manufacturing industry. Several studies have focused on sectors such as services, trading, and insurance. Research specifically addressing clustering in the manufacturing sector is limited. Existing clustering efforts in manufacturing revolve around groupings related to product defects, industrial locations, business ecosystems, and similar factors. Analysis of the methods, scope, and criteria used in grouping business process models in the manufacturing industry indicates that most approaches rely on structural or graphic similarities. Follow-up research is lacking once these business process clusters are identified. This study proposes a novel approach to grouping, integrating business process modeling with the implementation of a management information system. Business process management relies on integrating departments within the company through an enterprise resource planning (ERP) system. The next step involves proposing a conceptual framework to categorize business processes and assess the comparability of models in the manufacturing sector. Future research directions are also delineated.

Computer prediction of toxicity of new S-alkyl derivatives of 1,2,4-triazole

1,2,4-Triazole derivatives are of researchers’ significant interest due to their diverse biological properties, such as antimicrobial, anti-inflammatory, anticancer, and antioxidant activities. The integration of a 2-bromo-4-fluorophenyl fragment into the triazole structure can significantly enhance these activities. However, the evaluation of the toxicity of such compounds remains a critically important aspect for their practical application. To reduce the time and cost of experimental studies, QSAR (Quantitative Structure-Activity Relationship) methods are actively used, allowing the prediction of toxicity based on the molecular structure of compounds. Aim of the study. To assess the toxicity of new S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-R-1,2,4-triazol-3-thiols using the QSAR method, specifically to predict acute toxicity parameters (LD50), and to determine the influence of different (length) radicals on the toxicity of these compounds. Materials and methods. The objects of the virtual study were derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols. They were evaluated at the Department of Toxicological and Inorganic Chemistry of the Zaporizhzhia State Medical and Pharmaceutical University. The toxicity assessment was conducted using the nearest neighbor method via the Toxicity Estimation Software Tool (TEST). The prediction of the lethal dose (LD50) for rats was based on the structural similarity of the studied compounds with known substances, for which experimental toxicity data are available. Results. The conducted QSAR analysis demonstrated that structural changes in S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols significantly affect the predicted toxicity. The primary factor influencing the changes in LD50 values is the variation of radicals at the 5th position of the triazole ring. Conclusions. The results of the study showed, that the toxicity of new S-alkyl derivatives of triazol-3-thiols depends on the type of alkyl substituent. Compounds with propyl to heptyl fragments exhibit increased toxicity, while derivatives with thiol, octyl, nonyl, and decyl residues are characterized by lower toxicity.

Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.

Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images. We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of vision transformer (ViT) layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2-fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N=400), validation (N=50), and test (N=51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models with the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT-PSNR: 31.0±2.1, MSE: 0.009±0.0005, SSIM: 0.950±0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR-ViT model performs favorably against the ground truth data. We show that high-quality ADC maps can be synthesized from structural MRI using a MPR-ViT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable.

QSAR prediction of toxicity for a new 1,2,4-triazole derivatives with 2-bromo-5-methoxyphenyl fragment

New derivatives of 1,2,4-triazole are promising research targets due to their unique biological properties, including antimicrobial, antifungal, antitumor, and antioxidant activities. The introduction of the 2-bromo-5-methoxyphenyl fragment into the triazole structure potentially enhances these properties. However, the issue of toxicity for such compounds remains a critical factor for their further application. To reduce experimental costs and time, QSAR (Quantitative Structure-Activity Relationship) methods are widely applied, allowing to predict compounds toxicity based on their molecular structure. The aim of this study was to evaluate the toxicity of new derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, their acids, and esters using the QSAR method to predict parameters of acute toxicity (LD50) and to assess the influence of various radicals on the toxicity of the compounds. Materials and methods. The objects of this study were derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, synthesized at the Department of Toxicological and Inorganic Chemistry of Zaporizhzhia State Medical and Pharmaceutical University. The nearest neighbor method was used for toxicity evaluation, applying the Toxicity Estimation Software Tool (TEST). The prediction of rats lethal dose (LD50) was based on the structural similarity of the studied compounds with known substances that have experimental toxicity data. Results. The QSAR analysis revealed that structural modifications in the derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols significantly influence their toxicity. Specifically, increasing the size of the radicals, especially through the introduction of aromatic fragments, contributed to the enhanced safety of the compounds, as evidenced by the increase in LD50 values. The highest LD50 values were observed for compounds containing phenyl radicals. Conclusions. The results of this study indicate the feasibility of using QSAR models to predict the toxicity of 1,2,4-triazole derivatives containing a 2-bromo-5-methoxyphenyl fragment. The observed trend of increasing safety with the introduction of larger aromatic radicals can be used for the rational design of new compounds with improved toxicological properties.

Integrating Super-Resolution with Deep Learning for Enhanced Periodontal Bone Loss Segmentation in Panoramic Radiographs

Periodontal disease is a widespread global health concern that necessitates an accurate diagnosis for effective treatment. Traditional diagnostic methods based on panoramic radiographs are often limited by subjective evaluation and low-resolution imaging, leading to suboptimal precision. This study presents an approach that integrates Super-Resolution Generative Adversarial Networks (SRGANs) with deep learning-based segmentation models to enhance the segmentation of periodontal bone loss (PBL) areas on panoramic radiographs. By transforming low-resolution images into high-resolution versions, the proposed method reveals critical anatomical details that are essential for precise diagnostics. The effectiveness of this approach was validated using datasets from the Chungbuk National University Hospital and the Kaggle data portal, demonstrating significant improvements in both image resolution and segmentation accuracy. The SRGAN model, evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, achieved a PSNR of 30.10 dB and an SSIM of 0.878, indicating high fidelity in image reconstruction. When applied to semantic segmentation using a U-Net architecture, the enhanced images resulted in a dice similarity coefficient (DSC) of 0.91 and an intersection over union (IoU) of 84.9%, compared with 0.72 DSC and 65.4% IoU for native low-resolution images. These results underscore the potential of SRGAN-enhanced imaging to improve PBL area segmentation and suggest broader applications in medical imaging, where enhanced image clarity is crucial for diagnostic accuracy. This study also highlights the importance of further research to expand the dataset diversity and incorporate clinical validation to fully realize the benefits of super-resolution techniques in medical diagnostics.